icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | NAHL | 46 | 26 | 16 | 91.9% | 2.89 | 4 | 0.9843 | 92.5% |
| 2021-22 | — | SHL | — | — | — | — | — | — | 1.0100 | — |
| 2021-22 | — | SuperElit | 18 | 11 | 5 | 89.9% | 3.02 | 2 | 0.9600 | 88.2% |
| 2021-22 | — | SHL-J20 | 18 | 11 | 5 | 89.9% | 3.02 | 2 | 0.9600 | 88.2% |
| 2020-21 | — | SuperElit | — | — | — | — | — | — | 0.9600 | — |
| 2020-21 | — | SHL-J20 | — | — | — | — | — | — | 0.9600 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Merrimack | D1 | — | — | — | — | — | — |
| 2024-25 | Merrimack | D1 | 11 | 4 | 6 | 87.1% | 3.50 | 0 |
| 2023-24 | American International | D1 | 33 | 19 | 12 | 92.0% | 2.27 | 0 |
Historical goalies with similar age-adjusted SVe profiles who went on to play NCAA D1.
| Name | Junior League | Junior SV% | Adj SVe | College | NCAA SV% | NCAA GAA |
|---|---|---|---|---|---|---|
| Danick Leroux | NAHL | 92.7% | 92.6% | Holy Cross | 90.6% | 2.62 |
| Luca Di Pasquo | BCHL | 92.2% | 91.1% | Michigan | 91.0% | 2.41 |
| Johnny Hicks | BCHL | 94.3% | 93.5% | Denver | 95.5% | 1.25 |
| Petter Wickström Stumer | NAHL | 91.4% | 91.6% | Canisius | 90.4% | 3.52 |
| Carson Cherepak | MJHL | 92.1% | 89.2% | RPI | 89.6% | 3.11 |
Historical goalies with similar age-adjusted SVe profiles who went on to play NCAA D2/D3.
| Name | Junior League | Junior SV% | Adj SVe | College | Div | SV% | GAA |
|---|---|---|---|---|---|---|---|
| Brody Haynes | NCDC | 92.5% | 92.3% | Elmira | D3 | 94.8% | 1.22 |
| Justin Damon | USPHL-Premier | 94.0% | 91.5% | Gustavus Adolphus | D3 | 92.9% | 2.27 |
| Brayden Ingram | EHL | 91.2% | 94.4% | Assumption | D2 | 91.4% | 2.71 |
| Connor Carbo | EHL | 89.3% | 90.3% | Wentworth | D3 | 92.3% | 3.00 |
| Marko Belak | MJHL | 90.3% | 91.2% | Gustavus Adolphus | D3 | 87.8% | 3.08 |
How to read this: NCAAe and D3e factors convert a player's junior PPG into expected NCAA scoring at the D1 or D3 level. Harder conferences → lower projected PPG for the same player. A strong junior player (e.g. USHL 0.90 PPG) will project much higher in NESCAC than Big Ten because the D3 scoring environment is lower-difficulty.
Strength factor: conferences above 1.0 are harder than average; below 1.0 are easier. The formula is: Base NCAAe PPG ÷ Conference Strength = Projected PPG.